The City of Paris is made up of over 5000 private and public roadways, open to traffic, totaling almost 1500 kilometers (over 900 miles) of streets – of which 196 kilometers (120 miles) is equipped with sensors in order to optimize traffic flows in the nation’s capital.

In Paris today, the state of the streets is ascertained by municipal agents who roam the fiels and visually spot any damage. Though this data may be collated in specific software, it is far from exhaustive, precise or clear over time.

Paris City Hall’s application “Dans Ma Rue” (In My Street), which allows locals to flag any anomalies and propose improvements, helps to consolidate certain data, but even with help from crowdsourcing, this feedback is not enough. Today, roadworks are almost exclusively carried out in cases where damage endangers users and traffic flows.

Paris City Hall’s Streets and Transport Department is made up of 1276 agents and spends over 750 million euros every year to maintain one of Paris’ favorite means of transport.

That said, cars are increasingly connected and are capable of upstreaming unprecedented quantities of data.

With this in mind, the team at Renault has put together an algorithm capable of providing the state of the roadways thanks to the information provided by an accelerometer. Heavy vehicles equipped with sensors are already crisscrossing the country along major automotive arterials in order to upstream this kind of information to the companies in charge of their maintenance. Well-equipped cars provide more minute information regarding roadways. Their cost and/or their size are prohibitive with regards to their use in urban agglomerations, which is not the case of a solution using individual cars. It is time to use the data created by those who use the streets in order to work together to improve urban problems.

How can we offer a predictive roadway maintenance service using data gathered by connected cars ?

Experiments' objectives

Thanks to sensors on board vehicles, Renault is now able to produce a “state of the roadways” report. That said, the automotive company is as yet unable to translate this “state of the roadways” into maintenance offers, or even to integrate a predictive system. The objective for experimentation will be to produce a service who will provide maintenance offers according to the state of the roadways and the degradation speed, while also cross-referencing upstreamed data from vehicles and data from the City of Paris infrastructure.

This will involve exploring the creation of a predictive maintenance service.

In the end, the objective is to optimize maintenance costs and minimize any deployment of municipal agents, to organize multi-faceted interventions and anticipate any associated necessary traffic flow modifications. There is much to be gained in moving from a reactive repair model to a predictive maintenance model, as much in terms of quality of service as in financial terms.

Business Opportunities for Participating Stakeholders

Renault

Transformation of raw data into a service for municipalities.

Startup

The startup could be the service provider, and instigate a partnership with Renault to offer the service to municipalities or companies which manage road networks.

City of Paris

Optimization of roadway maintenance and associated costs.

Resources

Only one source of data currently enables the upstreaming of state of the road via vehicles, a specifically installed box;

Other data is accessible via roadway agents and City of Paris’ application “Dans Ma Rue”;

Tomorrow, other data will be upstreamed by vehicles, firstly certain extracts from a dongle plugged directly into the vehicle’s OBD jack, and then all the data from the CAN. Today we cannot say if this data can be correlated with that of the specifically installed box in order to detect roadway quality. The idea of this challenge will not necessarily be to work on this particular correlation.